A new coupled map car-following model considering drivers’ steady desired speed

Based on the pioneering work of Konishi et al., in consideration of the influence of drivers＇ steady desired speed ef/ect on the traffic flow, we develop a new coupled map car-following model in the real world. By use of the control theory, the stability condition of our model is derived. The validity of the present theoretical scheme is verified via numerical simulation, confirming the correctness of our theoretical analysis.     

考虑最邻近前车综合信息的反馈控制跟驰模型

拥堵控制中, 通过车辆运行状态感知与控制的交互融合, 实现对车辆有效控制的过程, 具有信息物理融合系统的典型特征. 本文基于Konishi等的研究工作, 从交通信息系统与交通物理系统融合的角度, 进一步考虑优化速度差和安全间距对车流的影响, 在耦合映射跟驰模型中, 提出了一种考虑最邻近前车综合信息的交通拥堵反馈控制方案. 运用反馈控制理论, 给出了头车速度发生变化时交通流保持稳定的条件, 并与前人工作进行了比较. 理论分析与数值模拟结果一致表明, 耦合映射跟驰模型在本文提出的控制方案下能更有效地抑制交通拥堵. 关键词： 交通流 交通拥堵控制 耦合映射跟驰模型 信息物理融合系统     

道路交通流状态的多参数融合预测方法

针对道路交通流普遍存在的混沌特性以及单交通参数不足 以全面反映交通流状态的实际情况,考虑交通动力学系统中多个 交通参数之间的关联关系,提出一种新的多参数混沌时间序列预 测算法.该算法在相空间重构理论的基础上,借助Bayes估计将多个参数在 同一高维相空间中进行相点最优融合,从而增加重构相空间的系统信息量, 使得相空间的相点轨迹更加逼近原交通系统的动力学行为.同时借鉴单 参数混沌时间序列预测方法,从不同角度对动力学系统的运动状态进行描 述,以实现多参数时间序列的混沌预测.实验结果表明,通过融合多交通参数时 间序列,获得了更加完整的交通流状态变化特征.与单交通参数时间序列的预测 结果相比,其预测误差显著降低,均衡系数相应增大,提高了交通流状态预测的准确率.     

A traffic flow lattice model considering relative current influence and its numerical simulation

<正>Based on Xue's lattice model,an extended lattice model is proposed by considering the relative current information about next-nearest-neighbour sites ahead.The linear stability condition of the presented model is obtained by employing the linear stability theory.The density wave is investigated analytically with the perturbation method.The results show that the occurrence of traffic jamming transitions can be described by the kink-antikink solution of the modified Korteweg-de Vries(mKdV) equation.The simulation results are in good agreement with the analytical results,showing that the stability of traffic flow can be enhanced when the relative current of next-nearest-neighbour sites ahead is considered.